ACEEE Int. J. on Electrical and Power Engineering, Vol. 02, No. 03, Nov 201 1
Speed Conrol of Separately Excited dc Motor using
Fuzzy Technique
Ram kumar karsh 1 , Dr. GK.Choudhary 2 , Chitranjan Kumar 3
'Dept. of Electrical Engineering, NIT Patna, India
1 tnramkarsh@gmail.com
2 H.O.D, Dept. of Electrical Engineering, NIT Patnajndia
(girishkrchoudhary, chitranjan_kumar24) @yahoo.co.in
Abstract This paper presents the speed control of a separately
excited DC motor using Fuzzy Logic Control (FLC). The Fuzzy
Logic Controller designed in this study applies the required
control voltage based on motor speed error (e) and its change
(ce). The performance of the driver system was evaluated
through digital simulations using Simulink. The simulation
results show that the control with FLC outperforms PI control
in terms of overshoot, steady state error and rise time.
Keywords: DC motor, chopper, FLC.
I. Introduction
DC motors are used in many applications like electric
trains, vehicles, cranes and robotics manipulators. They
require controlling of speed to perform their tasks. Initially
speed control of DC motor has been done by voltage control
[1]. Semiconductors too like MOSFET, IGBT and GTO have
been used as switching devices to control speed[2].
Due to nonlinearity properties, control of system is
difficult and mathematically tedious. To overcome this
difficulty, FLC (Fuzzy Logic control) has been developed.
FLC is applicable to time variant and nonlinear. Metro system
in the sendia of japan is the best application [3].
In this study, the speed response of a separately excited
DC motor exposed to fixed armature voltage is studied for
both loaded and unloaded operating conditions. Performance
of separately excited DC motor is compared for both methods
FLC and PI controller for both loaded and unloaded
conditions. In this study, chopper circuit has been used as a
motor driver.
II. Motor model
The resistance of the field winding and its inductance are
represented by R f and L respectively. The armature, resistance
and inductance are represented by R and L respectively.
Armature reactions effects are ignored in the description of
the separately excited DC motor. This negligence is justifiable
to minimize the effects of armature reaction since the motor
(SE) used has either interpoles or compensating winding.
The fixed voltage V is applied to the field and the field current
settles down to a constant value. A linear model of a simple
separately excited DC motor consists of a mechanical equation
and electrical equation as determined in the following
equations:
©2011 ACEEE
DOI:01.DEPE02.03.533
J,
d(a n
dt
^„(pIabco m M
load
L
dt
V I.R K h 0co
(1)
(2)
The dynamic model of the system is formed using these
differential equations and Matlab Simulink blocks as shown
in Fig. I,
31
%:•.
<:>
I — y^
o
_Si
:j
iijiii
©
i
■
;.;
■.»
mjmti
C"
i: j :
«■♦
+0
Fig 1: Simulink Motor mode]
Table I. Motor Parameters
Parameter
Description
Value
Ra
Armature
Resistance^)
0.5
L=
Armature
Inductance (H)
0.003
J*
Inertia of
rvlotor{kg.m ■*' £)
0.0167
K
Motor
c onstantfNm' Amp)
O.S
B
D amping ratio of
mechanical
systemptas)
0.0167
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ACEEE Int. J. on Electrical and Power Engineering, Vol. 02, No. 03, Nov 201 1
III. Fuzzy Logic Controller (FLC) Description And Design
Fuzzy logic control is based on logical relationships like
"suitable, not very suitable, high, little high, much and far
too much that are frequently used words in people's life.
Fuzzy Sets Theory has been introduced to express and process
fuzzy knowledge [4], [5] which are used to show linguistic
variables. The relation between fuzzy logic and fuzzy set
theory that is similar that of relation between Boolean logic
and set theory. Fig. 2 shows a basic FLC structure.
Fully™*!
"♦PUfflHlHJlg
fasfaaw
■;';.=

PWprMaflf
Fig. 2 Process Blocks for a Fuzzy Controller
FLC is processed for linguistic definitions, while other
contrllers work on the accuracy and parameters of system
model. While designing FLC, there is no need of knowledge
of system model, as a controller. However, less knowledge of
control process may result unsatisfactory [6].
A. Defining inputs, outputs:
As the bigger speed error the bigger controller input is
expected. So, FLC is designed to minimize speed error. Due to
that FLC uses error (e) and change of error (ce) for linguistic
variables which are generated from the control rules. Control
variable (cu) is applied to achieve angular value (alpha), which
determines duty cycle.
e(k)=[co(k)co(k)]*K
IE
ce(k)=[e(k)e(kl)]*K
ca(k)=[a(k)a(kl)]*K
2CE
3 co.
(3)
Here K 1E K, CE and K 3 ca are each gain coefficients and K is a
time index.
wr[k]
9
&>■%
hH
(UilYWrtlr*
%MflW
N
Fig. 3. Block diagram of the DC motor control
At nominal value of motor speed the error (e) gives its smallest
value, and at maximum value of motor speed the error gives
its larger value, with range 200 and 200.
©2011 ACEEE
DOI:01.DEPE.02.03.533
1
eHH^**rtr^
^ A/Y^^*  ' —
QJ2L
~rg
^J¥"
+ ,[.
4J
— ™ t i :  t j™
a =
\
I
...., .j 1 ;.,,.._ _j.„, j..^
n n
1
M
z :
■i
1
w
:iziz:jz:iz::n ... 1 ...:
1 7
1
I
■ 1
\
r
— ;  ; [ t H
1 h
 1
F+
Fig. 4. Change of Error
W
Ai
A^
As
A.
A 5
A«
A?
M
As
Am
e
+





+
+

ce


—
—


—
+


Fig. 5. Dynamic Signal Analysis
B. Defining membership functions and rules:
System speed comes to reference value by means of the
defined rules. For example, first rule on Table determines, 'if
(e) is PL and (ce) is PL than (c.) is NL According to this rule,
if error value is positive large and change of error value is
positive large than output, change of alpha will be negative
large. In this condition, corresponding A4 interval in Fig 5,
motor speed is smaller than reference speed and still wants
to decrease strongly. This is one of the worst conditions in
control process. Because of the fact that alpha is smaller
than the required value, its value can be increased by giving
output PL value. This state corresponds to motor voltage
decreasing. All conditions in control process are shown in
Fig.5. Membership functions have been used to convert
inputs and outputs from crisp value to linguistic term.
Linguistic terms are represented here by seven membership
functions shown in table.
.
Fig. 6. Linguistic rules for angle (alpha) determination for driver
circuit. It will for a) speed error, b) Change in speed error, c)
32
Change of alpha
ACEEE
ACEEE Int. J. on Electrical and Power Engineering, Vol. 02, No. 03, Nov 201 1
Table II. The Rule Data
Base
ee
m
NM
XS
Z
PS
PM
PL
m.
PL
PL
PL
PL
mi
2
2
XM
PL
PL
PL
P.I/
PS
2
2
m
PL
PM
PS
PS
PS
2
2
z
PL
PM
Pi
2
NS
KM
KL
p&
2
2
KM
KS
KS
KM
M
PM
2
2
NS
KM
KL
M
M
PI
2
2
MM
M
M
M
M
IV. Driver circuit and modeling
DC chopper has been used to drive the motor also changes
average value of load voltage applied from a fixed DC source
by switching a power switch.
I.
y.
Vo
Load
Vo
i ton,
■* — *■■*■
i©r
■\
I
Fig 7. Operating principle and output waveform of Driver
Using Fig 7, the average output voltage can be calculated as
V
t,
do
t +t«
l on I off
V
(4)
Where V is the DC source voltage, v, can be controlled
o do
using three methods:
*Hold t fixed and change t (frequency modulation)
*Hold period (t + t ff ) fixed and change t ff /t rate (pulse
width modulation)
*change t fl and t separately. (Combination of first and
second method)
Onequadrant DC chopper and general waveforms for
continuous current conditions are shown in fig. 8
(Xjwi: nflcli
Fig 8. simple power circuit of a one quadrant DC chopper
10
«
imax
irm
iD
imin
Vo
DT
fr
I
0T T
Fig 9. General waveform for current continuous condition
Fig. 10. DC Chopper model
Fundamentally, the operating principle of driver model is
based on the comparison of two signals [7] . One of the sig
nals is a triangular waveform which represents one PWM is
used to control average output voltage period of 2 KHz chop
ping frequency and other one is fixed linear signal which
represents time equivalent of alpha triggering (t ). Since
chopping frequency is 2 KHz, the amplitude of triangular
waveform starts from zero and reaches 1 / 2. 10 3 = 0.0005 value.
On the other hand, the alpha signal from controller is multi
plied by 0.0005/360 value to calculate the time corresponding
to this angle. Alpha signal and triangular signal are Uj and
U2 variables of ' IF' block used in simulation model shown in
Fig. 10, respectively.
•.+
OuOOOS
v ...
vdo
IN
1 tfs} — '
Fig 11. input and output signal of driver model
©2011 ACEEE
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ACEEE Int. J. on Electrical and Power Engineering, Vol. 02, No. 03, Nov 201 1
V. Control simulation
In FLC model gainl, gain2 and gain3 define change of error,
error and change of alpha scaling factors respectively. Simu
lation results are shown for 50 nm load applied at 0.6s. Simu
lation result for PI controller for loaded and no loaded condi
tion is shown in table iii
IZZ1
S:tf*2
Aitf
f I
I )
■
M
■ .
■
' .... . ■:.
H»
r
:n
Slit*'
Fig 12. Fuzzy Logic Controller Simulink model
Fig 13. a) Speed response of PI controller for used motor
Fig 13. b) Speed response of Fuzzy controller for used motor
Table III performance analysis of system
CI: Kp=100, Ki=15 Tr: Rise time
C2: Kp=200,Ki=25 e : Steady state error
C3: Kp=300, Ki=28 %M Percentage Maximum overshoot
C4: Kp=500, Ki=30 CI, C2 Different Kp and Ki
coefficients
(a) For different loads
p
I
Land
Jtf.Y
soy
5GX
CI
SO£
5.955
y.Cii
5 52
■?„
0.5Q5
0.2SS
0.131
C2
'A.%
5.655
5. till 5
5.631
s„
6,25
D.15S
0.15
C3
%14
5. '31 5
5:7315
5:7315
s„
0.1S
0.15
0.12
C4
%u.
i.S5S
5.5S5
5.S95
■?„
6.1175
0.05
0.075
FLC
5fl4
0.S1
2.64
4.53
•Br,
Q.QOdl
0.0045
0.01S
(b) Unloaded operation
Criteria
PI
FLC
CI
C2
CS
C4
Tr
0.141
0.141
'■ !i!
0.141
0.036
#r,
0.533
0.5 '5
0.415
0.262
0.01
&w f
5.527
5.631
5:711
5.S94
0.6S
Percent overshoot (%M ) and steady state error (e ) are
measured for different load.
Conclusion
Fuzzy Logic Controllers are a suitable option to make
speed regulation in DC motors and AC motors. The quality of
the control obtained with FLC's at the first tries is commonly
good because is based on the knowledge of an expert. It can
not be said the same about conventional controllers. The
single human based reasoning used on a FLC can be very
useful to overcome nonlinearities of any kind of plants in a
logical way. The experience gained from these works has
allowed us to attack another systems of very different nature
obtaining satisfactory results. Comparison between PI
controller responses and FLC responses is shown in table iii
and shows that FLC gives better performance than PI
controller in terms of overshoot , steady state error and rise
time. Also show that FLC is more sensitive to load changes.
It would be necessary to use a more complex intelligent control
system, i.e. Adaptive Fuzzy System, NeuroFuzzy System, in
order to obtain a better performance on speed control
Refrences
[I] Chan, C. C, Low Cost Electronic Controlled Variable Speed
Reluctance Motors, IEEE Transactions on Industrial Electronics,
Vol. IE34, No. I. 95100. February 1987.
[2] Khoei, A.. Hadidi, Kh., Microprocessor Based ClosedLoop
Speed Control System for DC Motor Using Power Mosfet.
Electronics Circuits and Systems IEEE international Conference
ICECS'96, Vol. 2, 12471250, 1996.
[3] C. Elmas, "Fuzzy Logic Controllers", Seqkin Publishing, April
2003
[4] L. A. Zadeh, " Fuzzy Sets" Informal Control, 01. 8p, p 338
353, 1965.
©2011 ACEEE
DOr.Ol.LTEPE.02.03.533
34
vc ACEEE
ACEEE Int. J. on Electrical and Power Engineering, Vol. 02, No. 03, Nov 201 1
[5] L. A. Zadeh, '. Outline of a new approach to the analysis
complex systems and decision processes" IEEE Trans. Syst. Man
Cybem, vol. SMC3, pp. 2844, 1973
[6] Y. Tipsuwan, Y. Chow, "Fuzzy Logic Microcontroller
Implementation for DC Motor Speed Control". IEEE. 1999.
[7] F. Rahman, .'Lectures 18 Control of E€DC Conveners", Power
Electronics. ELEC424019240.
Ram kumar Karsh was born in 1986 in
a small village of lanjgirchampa district
of Chattisgarh. He received B.E. (Electron
ics & Tele communication) in 2009 with
first class from Govt Engineering College,
Bilaspur, India. He is pursuing M.Tech.
degree with specialization Control Sys
tems from Department of Electrical Engi
neering, National Institute of Technology (NITP), Patna, India.
His field of interest includes fuzzy logic, control systems.
Prof. Girish Kumar Choudhary was born
in August, 1959 in a small village. He did his
matriculation in 1975, and was enrolled for
diploma in Electrical Engineering in the same
year. He completed his diploma in Electrical
Engineering securing first position in the
state of Bihar in 1979. He got his B.Sc
(Engineering) degree in Electrical in 1985 securing first class first
from Patna University, Patna with distinction. He acquired his
Ph.D degree in 1998 from Patna University. He joined BCE Patna
(Presently NIT Patna) in Ian. 1988 as Lecturer in Electrical
Engineering and promoted as Associate Professor Electrical
Engineering in 1996. Subsequently he became Professor in 2006.
Presently he is working as Professor & Head of Electrical
Engineering at NIT Patna. He is also holding the post of Chairman,
HMC, NIT Patna. He has many publications in National and
International lournals and Conferences. He has also achieved the
distinction of getting his research product "Adapters for Laptops
and others Electronic Devices." Patented (No. 235642 dated
10.07.2009). Prior to joining NIT Patna he has also worked in All
India Radio (AIR) Patna and Videsh Sanchar Nigam Limited, Arvi,
Pune.
Chitranjan kumar received B.E. (Elec
tronics & Tele communication) in 2009
with first class from D.K.TE.S Ichalkaranji
,Kolhapur, India. He completed M.Tech.
degree with specialization Control Systems
in 2011 with first class from Department
of Electrical Engineering from National In
stitute of Technology (NITP), Patna, India. His field of interest
includes fuzzy logic, signal processing, control systems
©2011 ACEEE
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^ACEEE